142 results about "Visual methods" patented technology

The invention discloses a multi-target scene visual SLAM (Simultaneous Localization and Mapping) method fusing target semantics and Gaussian splashing. The method comprises the steps that input data are preprocessed, two-dimensional space masks and semantic information corresponding to the two-dimensional space masks are extracted, global semantic identifiers are distributed to the two-dimensional space masks corresponding to a background and each target, and a target semantic segmentation map is obtained; reading first frame RGBD data as a current frame, acquiring a target semantic segmentation map corresponding to the current frame, and initializing camera pose parameters; respectively establishing an initial target Gaussian splashing model for the background and each target; adopting a Gaussian splash algorithm to render all the target Gaussian splash models to a next frame, and generating an RGB rendering image, a depth rendering image and a target semantic rendering image of the next frame; and designing a loss function between the next frame of rendered image and the corresponding real image, optimizing the pose of the camera, and updating the parameters of the target Gaussian splash model. According to the invention, simultaneous positioning and map construction of multiple target scenes are realized.

A dynamic vision SLAM (Simultaneous Localization and Mapping) method based on an extended Bayesian model belongs to the technical field of autonomous robot navigation and computer vision crossing, and mainly comprises the following steps: performing prior dynamic object recognition on a current frame image by using an improved PWt-YOLO network, and outputting a binary mask and semantic probability distribution; oRB feature extraction is carried out by using a hierarchical quadtree algorithm and combining an adaptive threshold value; establishing an extended Bayesian probability model, fusing semantic prior, optical flow residual and epipolar geometric constraints of two-dimensional Gaussian distribution constructed based on dynamic object boundaries, realizing time sequence transmission of dynamic feature probabilities through a Markov chain, and executing feature point filtering according to joint dynamic probabilities; and robust pose estimation is realized through RANSAC-PnP, a sliding window and the like. According to the method, the problem of SLAM system positioning drift in a dynamic environment can be effectively solved by constructing a multi-modal fusion dynamic feature discrimination system.

The invention relates to the technical field of data signal processing, in particular to a distribution box electric spark monitoring method which has the advantages of not depending on optical imaging conditions, adapting to complex environments and being capable of effectively distinguishing difference between mechanical vibration and discharge signals. According to the invention, non-intrusive monitoring of electric sparks in the distribution box can be realized, and the limitation of a traditional visual method in a closed space is overcome. And the fusion of the multi-dimensional acoustic features improves the recognition accuracy and reduces the false alarm rate. The introduction of environmental parameters enables the system to adapt to different working conditions, and improves the robustness of monitoring. The method does not need to reform the structure of the distribution box, can be widely applied to state monitoring of various low-voltage distribution equipment, and effectively prevents equipment faults and safety accidents caused by electric sparks.

The invention discloses a dynamic scene robust vision SLAM (Simultaneous Localization and Mapping) method based on multi-feature collaborative optimization, which comprises the following steps of: acquiring an image sequence, carrying out dynamic target detection and segmentation through an instance segmentation network, generating a segmentation mask containing a dynamic region mark, and identifying and separating a dynamic object and a static background; removing feature points corresponding to the dynamic object based on the segmentation mask to obtain static feature points; carrying out pose estimation based on the static feature points, and for the key frame, carrying out feature matching with the previous key frame by minimizing a re-projection error, and solving to obtain the camera pose of each key frame; for non-key frames, performing camera pose tracking and data association on the previous frame by adopting an optical flow algorithm, and accumulating solving results to obtain pose tracks of all the non-key frames; the key frames and the non-key frames are subjected to differential processing by fusing feature matching and an optical flow algorithm, so that the calculation efficiency is remarkably improved while the positioning precision is ensured, and the real-time performance is improved.

The invention discloses an image enhancement and visual SLAM (Simultaneous Localization and Mapping) method and system aiming at a low-illumination scene. The method comprises the following steps: firstly, judging by utilizing an image brightness detection algorithm, and carrying out image brightness adjustment on an image with abnormal current brightness; then carrying out image brightness adjustment based on a mean value adaptive Gamma value, calculating Gamma correction indexes corresponding to different low-illumination images with abnormal brightness, and carrying out brightness correction on the different low-illumination images according to the Gamma correction indexes; and finally, carrying out image contrast adjustment based on a CLAHE algorithm, wherein the CLAHE algorithm is adopted to enhance the contrast of the image after the mean value self-adaptive Gamma correction. According to the method, the problems of poor positioning precision, low robustness, poor map quality and the like of a visual SLAM system in a low-light environment in the prior art are solved. According to the method, deep learning and a traditional visual SLAM framework are combined, image performance is changed through an image enhancement method to improve image quality, and a feature point extraction mode and matching precision in a low-light environment are improved.

The invention provides a monocular vision and sparse IMU rehabilitation action whole body posture estimation method and system, and the method comprises the steps: synchronously collecting video data and inertial data of human body rehabilitation actions through a monocular RGB camera and a plurality of IMUs, and cutting and zooming an image to a preset resolution; extracting a key point thermodynamic diagram from continuous N frames of images by using a sliding window and a residual neural network, and calculating 2D key point pixel coordinates of each frame; splicing the N frames of 2D key point pixel coordinates, the rotation matrix of the IMU and the acceleration signal into an input sequence; cross-modal time sequence modeling is carried out on an input sequence through time Transform, and after high-dimensional features are extracted, weighted average is carried out through a convolutional layer, and 3D relative key point coordinates of the last frame are output through a regression head. According to the method, by fusing monocular vision and sparse IMU cross-modal data, the problem of visual information loss caused by limb self-shielding is effectively solved, and the defect that a traditional pure vision method is insufficient in precision in rehabilitation actions is overcome.

The invention relates to a semantic dense vision SLAM (Simultaneous Localization and Mapping) method and system based on 3D (Three-Dimensional) Gaussian representation, and belongs to the field of body intelligence. The method comprises the following steps: acquiring image data of a scene through a sensor, constructing a scene model of 3D Gaussian representation, fusing semantic features and appearance features to generate high-dimensional semantic features, optimizing parameters of Gaussian points by combining appearance, geometry and semantic constraints, updating the scene model according to the optimized parameters, and optimizing a camera pose. And generating a rendered image through a differentiable rendering technology, tracking and positioning, detecting a loop by using image features, and optimizing the global map precision. According to the method, the mapping precision and robustness of the SLAM system are remarkably improved, so that the SLAM system can show more excellent performance in a complex environment.

The invention relates to a concrete mixing main machine visual monitoring system and a method thereof, and belongs to the technical field of concrete production automation and intelligent quality control, and the method comprises the following steps: S1, collecting a video image sequence of movement of concrete in a mixing main machine in real time; s2, extracting a dynamic visual feature vector and a static feature vector based on the video image sequence; s3, on the basis of the dynamic visual feature vector and a preset dynamic rheological-visual feature coupling model, calculating an internal rheological state index; s4, based on the static feature vector and a preset static correlation model, calculating a predicted value of the foundation slump; s5, determining a final corrected slump through a preset correction function in combination with the intrinsic rheological state index and the predicted value of the foundation slump; s6, according to the final corrected slump, the predicted value of the foundation slump and the intrinsic rheological state index, the comprehensive risk index is calculated, and the intrinsic rheological characteristics which are caused by chemical admixtures and cannot be perceived by a traditional visual method can be deeply captured.

The invention discloses a semantic vision SLAM (Simultaneous Localization and Mapping) method and a semantic vision SLAM system for an indoor low-texture and dynamic environment, which are characterized in that a visual odometer, a dynamic point elimination algorithm, a loopback detection algorithm and the like are integrated into a visual SLAM system, so that data in an indoor low-texture and dynamic scene are processed in real time, and high-precision positioning and high-quality map construction are provided. A semantic information acquisition thread and a dynamic point elimination module are added to the whole system, and a loopback detection module is improved. According to the method, the problems of poor positioning precision, low robustness, poor map quality and the like of a visual SLAM system in a low-texture environment and a dynamic environment in the prior art are solved, and the feature point extraction and matching precision in the low-texture environment is improved; the interference of a dynamic object on the SLAM system is eliminated, and the positioning precision and the loopback detection performance are improved; the map construction quality is improved, and the semantic map with high readability is constructed by fusing semantic information.

The invention discloses a dynamic semantic vision SLAM (Simultaneous Localization and Mapping) method based on point and line feature adaptive weighting. The method comprises the following steps: firstly, obtaining potential dynamic region prior by combining target detection and image segmentation; then adaptive weighting is carried out on point and line features in two stages: in the first stage, initial weighting is carried out based on motion consistency check and dynamic levels, and feature matching and pose preliminary estimation are realized; in the second stage, weighting is further carried out on the key frame level through multi-view consistency verification, and weighted optimization and map maintenance are combined; and finally, loopback verification and global optimization are completed based on weighted features, and cumulative drift is effectively eliminated. According to the method, interference of dynamic and low-credibility features is suppressed through differential feature weighting, the adaptability to an intermittent moving object is improved through a double-stage weighting mechanism, and the stability and observability of a low-texture scene are enhanced through point-line combined modeling.

The invention relates to the field of video frame insertion and binocular vision, in particular to an asynchronous binocular camera synchronous frame obtaining and positioning method based on binocular vision and video frame insertion, which comprises the following steps: S1, calibrating a binocular camera, and obtaining rough delay time of the binocular camera through testing; s2, acquiring asynchronous left and right camera images under the same clock, taking the left camera image as a reference view, and selecting two continuous frames of right camera images after rough delay time; s3, performing frame insertion on the two right camera images by using a frame insertion algorithm to obtain a series of right camera images; s4, obtaining an image with the highest synchronization degree with the left camera image in the series of images according to an optimal frame selection method; and S5, measuring the three-dimensional coordinates of the target point for the obtained synchronous left and right images by using a binocular vision method. The method overcomes the problems of high hardware synchronization cost and complex system in the existing binocular camera synchronization technology.

The invention belongs to the field of robot positioning and mapping algorithms, and provides a visual SLAM method and device for point-line feature fusion and a medium, and the method comprises the following steps: preprocessing an image, and obtaining point features and line features; matching the point features and the line features to obtain matched point features and matched line features; obtaining the initial pose of the camera through the matched point features and the matched line features, and constructing map points; constructing a three-dimensional map straight line through the matched line features; and optimizing the three-dimensional map points and the three-dimensional map straight lines. Complementary advantages are formed through extraction, matching, mapping and optimization processes of tight coupling point features and line features, and the fundamental problem of insufficient features in a weak texture environment is effectively solved.

The invention relates to the technical field of machine vision, in particular to a self-adaptive sorting system and method based on machine vision. Parcel image data on the sorting line is collected through an image collection device, and the image data is analyzed and recognized in combination with a machine vision method, so that real-time parcel information is obtained; based on the real-time package information, the real-time running state of the sorting line is analyzed and obtained, and a self-adaptive control decision of the sorting line is executed according to the real-time running state; and according to the self-adaptive control decision result, the sorting line is controlled to execute self-adaptive sorting treatment. According to the invention, intelligent and automatic operation of the sorting system can be realized.

The invention relates to the technical field of computer vision and intelligent monitoring, and particularly discloses a pavement ponding detection method based on image calibration and dynamic region segmentation, which comprises the following steps of: firstly, selecting a reference frame image in a ponding-free or slight ponding state from a road monitoring video, and manually marking a ponding region to generate a reference mask; the method is used as a unified detection reference standard. And then, frame extraction is performed on a to-be-detected video, pixel-level identification is performed on each frame of image by using a pre-trained semantic segmentation model, a water accumulation area of a current frame is automatically extracted, and a current water surface mask is generated. And dynamically judging the diffusion or fading trend of the accumulated water by calculating the difference value between the pixel area of the current frame accumulated water area and the pixel area of the reference mask accumulated water area. According to the method, the defects that a traditional sensor is high in deployment cost and complex in maintenance and an existing visual method is prone to being interfered and cannot be quantitatively changed are overcome, and low-cost, high-adaptability and quantifiable continuous automatic monitoring and early warning on the road surface ponding range is achieved.

The invention relates to the field of computer vision and mobile robot navigation, and discloses an object-level semantic vision SLAM method and system based on a mixed attention mechanism target detection network and an ellipsoid model. The system comprises five parallel thread modules, namely a semantic perception module, a visual tracking and repositioning module, a local mapping and fusion module, a loopback detection module and a global consistency optimization module. According to the method, local and global features of an image are extracted in parallel through a target detection network, and high-precision semantic observation is output; when the tracking is lost, the dual geometric constraint of the 2D internally tangent ellipsoid-3D object ellipsoid is utilized, and the P3P algorithm and the IoU cost function are matched to realize rapid relocation. In the mapping process, Gaussian-Wasserstein distance is introduced to measure semantic re-projection errors, and a joint objective function containing map point geometric errors and object semantic errors is constructed to carry out bundle adjustment. According to the method, the problem of feature extraction failure in motion blur and weak texture scenes is effectively solved, and the construction precision of the semantic map and the survivability of the system are remarkably improved.

The invention discloses a neural implicit vision SLAM (Simultaneous Localization and Mapping) method based on dynamic perception. The method aims at solving the core technical problems that an existing visual SLAM method is insufficient in robustness, poor in global consistency, large in calculation overhead and the like in challenging environments such as dynamic scenes, weak texture areas and violent illumination changes. According to the method, the feature processing capability of deep learning, efficient dynamic object perception, advanced neural implicit mapping and a global optimization mechanism are integrated, so that more accurate camera pose estimation and higher-quality static environment map construction are realized. In the tracking module, a six-step workflow based on mask guidance is adopted, dynamic objects are filtered from the source, frame-level pre-screening is carried out, and the robustness and the calculation efficiency of the system are remarkably improved. In a dynamic local mapping module, a pixel-level fusion method based on transmission probability and inverse variance weight is innovatively adopted, texture blurring and geometric distortion at the boundary of a plurality of sub-maps are effectively inhibited, and the visual quality of a global map is improved. Besides, by introducing a loop candidate frame reordering strategy based on pose uncertainty weighting in loop detection, visual similarity and geometric credibility can be combined, the false detection rate is effectively reduced, and global consistency and long-term precision of the map are ensured.

The invention belongs to the field of bridge detection and evaluation, and discloses a bridge line shape detection and damage prediction method based on a smart phone and a hybrid neural network, and the method comprises the steps: obtaining a bridge image through the smart phone by employing a distributed vision method, and obtaining vertical acceleration response data and environment temperature when a standard vehicle passes through a bridge through a mobile phone sensor; a YOLOv5s-CECA target detection model is used to obtain the pixel coordinates of the feature points; taking gyroscope attitude parameters during shooting as initial external parameters, converting positioning feature point pixel coordinates into a world coordinate system in combination with a pre-calibrated mobile phone internal parameter matrix, optimizing feature point coordinates by minimizing a re-projection error, drawing an actual bridge line shape, and obtaining a target line shape. And inputting the obtained bridge data into the trained AFTCNN-LSTM model to obtain a damage degree prediction result. The method can be rapidly deployed in a complex engineering field environment, improves the efficiency of bridge line shape detection and damage prediction, and reduces the bridge detection and evaluation cost.

The invention discloses a visual SLAM (Simultaneous Localization and Mapping) method suitable for a weak texture environment. The visual SLAM method comprises the following steps: step 1, extracting point features based on SuperPoint features of a lightweight deep neural network; 2, constructing a mixed descriptor by combining lightweight SuperPoint feature points and line features, and improving the stability of pose solution by using geometric topology constraints; and step 3, introducing a YOLOv8-seg semantic segmentation network to filter a dynamic target. According to the method, a multi-modal feature fusion scheme is provided for the problem of feature point distribution imbalance of ORB-SLAM3 in a weak texture environment, the homology and distinction degree of feature matching are enhanced, and dynamic interference is reduced through a moving target mask. Experiments show that the method significantly improves the tracking precision and robustness of an SLAM system in a complex environment, and compared with traditional ORB-SLAM3, the average standard error on a TUM RGB-D data set is reduced by 73.4%, and the effectiveness of the method is verified.

The invention provides a visual SLAM method suitable for a low-illumination dynamic environment, and the method comprises the following steps: S1, obtaining an RGB image of a scene, converting the RGB image into an HSV color space, carrying out the brightness detection of a V channel of the image, improving a self-calibration illumination frame through a mixed attention mechanism, and carrying out the image enhancement of a low-illumination image; s2, utilizing an improved ORB feature point extraction method to perform feature point extraction on the images with different illumination intensities by applying an adaptive threshold value; s3, dynamic feature points in the image are extracted and filtered in combination with an instance segmentation network YOLOv8-seg and a motion consistency check method; and S4, performing inter-frame feature point matching on all static feature points in the obtained ORB feature points to obtain an optimal matching feature point, and performing camera pose estimation based on an RANSAC algorithm and the optimal matching feature point to obtain a camera motion result. According to the method, the problem that the existing visual SLAM navigation positioning cannot effectively resist the interference caused by the low-illumination scene and the existence of the moving object in the low-illumination and dynamic environment is solved.

The invention belongs to the technical field of intelligent navigation of agricultural machinery, and particularly relates to an autonomous navigation system of field unmanned transportation equipment after potatoes (such as potatoes and sweet potatoes) are harvested, in particular to a potato operation line identification method based on binocular camera multi-modal information dynamic weighting. Aiming at a complex field environment (soil is loosened and turned over, scattered potato blocks / weeds are mixed, and an original ridge-shaped structure is locally damaged) after the operation of a harvester, a ridge line track required by the driving of a transport vehicle is difficult to stably identify in a scene of strong light overexposure, weak light noisy points and shadow alternation by a traditional visual method. According to the method, an IntelRealSenseD456 active stereoscopic vision binocular camera is carried, RGB images and depth information are fused in real time, and multi-modal data are dynamically weighted based on illumination intensity, so that the robust perception capability of the unmanned transport vehicle on the geometric boundary of the potato ridge is improved, the vehicle is ensured to accurately run along a harvested field ridge operation line, and the situation that the potato blocks are rolled and scattered or deviated from a path is avoided.

The invention specifically discloses a target perception type dynamic visual SLAM method fusing point and line features, and relates to the technical field of visual SLAM. According to the method, a YOLOv8-seg model is combined with TensorRT reasoning to realize target detection and segmentation, and a DeepSORT algorithm is reconstructed to complete multi-target stable tracking; carrying out motion consistency check based on a luminosity consistency principle and an optical flow method; calculating a three-dimensional center of the target according to the detection frame, the mask depth and camera parameters to realize three-dimensional reconstruction of the target; point and line features are extracted and processed, and the line features are represented by Plcarbon coordinates; and constructing a nonlinear optimization model based on an ORB-SLAM3 framework, and jointly optimizing camera pose, map points and map line parameters. The method effectively improves the positioning precision and the system robustness in a dynamic scene, achieves the precise modeling of a static structure and the reliable generation of a dynamic target track, and is suitable for autonomous navigation scenes such as an unmanned aerial vehicle and a service robot.

The application discloses a visual SLAM method and system in a dynamic environment, and belongs to the technical field of visual images. The method comprises the following steps: acquiring image information and depth information acquired during movement of an RGB-D camera, so as to acquire a visual image and distance values of each pixel point; performing feature extraction on the visual image, so as to acquire a feature point set; performing target detection on the visual image, so as to acquire a potential dynamic region set; performing three-dimensional reconstruction on the feature points, so as to acquire three-dimensional space points; acquiring comparison results of the feature points and the three-dimensional space points and the potential dynamic region, so as to acquire dynamic suspicious points; updating a dynamic confidence degree, performing optimization demand verification on the dynamic suspicious points, so as to determine and suppress dynamic suppression points, and acquiring static feature points; performing pose optimization on the static feature points, so as to acquire stable static features, and performing three-dimensional reconstruction, so as to acquire a high-quality map. The method solves the problems of insufficient real dynamic suppression precision and easy misdeletion of static features in the prior art, and improves the construction quality of the map.

The invention relates to a method for patient registration on a medical visualization system (1), wherein an image (10) of a body part (21) of a patient (20) is captured by a camera (2) of the medical visualization system (1), wherein, based on the captured image (10), a three-dimensional surface profile (11) of the body part (21) is estimated in a coordinate system (30) of the camera (2) by means of a trained machine learning method (6) and / or a computer vision method (7), wherein a scaling factor of the three-dimensional surface profile (11) is determined and / or estimated, and wherein preoperatively captured three-dimensional patient data (22) which are present in a patient coordinate system (31) are adapted to the estimated three-dimensional surface profile (11),wherein, based on a resulting adaptation result, a transformation rule (T) is determined between the coordinate system (30) of the camera (2) and the patient coordinate system (31), wherein the adaptation and / or the determination of the transformation rule (T) are carried out taking into account the determined and / or estimated scaling factor, and wherein the determined transformation rule (T) is provided. Furthermore, the invention relates to a medical visualization system (1).

The invention belongs to the field of physical fitness testing equipment, and discloses physical fitness testing equipment and a working method, which realize full-automatic identification and standardized counting of physical fitness testing actions, support various physical fitness testing items, do not need manual intervention, greatly reduce the possibility of manual participation and errors, and improve the working efficiency. Compared with a traditional manual counting mode which depends on personal experience of supervisors and subjective judgment, the problems that results are poor in repeatability, standards are not uniform, and missing recording and mistaken recording are prone to occurring are effectively solved. Through deep learning key point detection and motion classification based on the KNN algorithm, the motion type can be identified more accurately, motion details can be analyzed, whether each motion meets the standard or not can be accurately judged, the motion standardization judgment precision is improved, and the problem that a traditional visual method is difficult to accurately identify the motion details and normalization is solved.

The application belongs to the technical field of visual SLAM, and specifically provides a visual SLAM method based on an improved SuperPoint in a dynamic environment, to solve the problem of insufficient robustness of the existing visual SLAM method in a dynamic environment; the application firstly constructs a feature point and descriptor extraction network with an added residual weight branch on the basis of the SuperPoint network, performs weighted fusion on the network residual weight branch output and the feature point branch output, so that the constructed network has the function of eliminating dynamic feature points; then the constructed network is applied to the tracking thread of ORB-SLAM2, to replace the ORB method to extract image feature points and descriptors; in combination with the local mapping and loop detection threads of ORB-SLAM2, a robust visual SLAM system in a dynamic environment is realized, and the accuracy and robustness of the visual SLAM system in the dynamic environment are improved.

The invention belongs to the technical field of image processing and computer vision, and particularly relates to a visual SLAM (Simultaneous Localization and Mapping) method and a visual SLAM system for adaptively optimizing a dynamic region, which are used for carrying out semantic screening based on a multi-class semantic set difference set method and solving the problem of dynamic and static overlapping. More importantly, the strategy of self-adaptive decision based on dynamic point density fundamentally solves the two problems that a bounding box contains too many backgrounds and a slow moving target is improperly processed in the prior art, and intelligent and differentiated processing of a dynamic area is achieved. Through the dual effects of semantic screening and self-adaptive decision making, the method can generate a dynamic area mask which is much more accurate than an original bounding box and an existing fixed process method, especially in scenes that a dynamic object is close to a lens and the like, the boundary can be intelligently shrunk, precious static features mistakenly deleted by a traditional method are reserved to the maximum extent, and the dynamic area mask is more accurate. And the precision and robustness of the SLAM system are directly improved.

The invention discloses a belt deviation detection device and method, and relates to the technical field of visual method detection and deep learning, and the belt deviation detection method comprises the steps: capturing a belt operation image, carrying out the belt position region labeling, constructing an image semantic segmentation network model, inputting the belt operation image into the image semantic segmentation network model, and carrying out the image semantic segmentation network model. Acquiring a belt position area; an image semantic segmentation network model is combined with an ASPP multi-scale feature extraction network, the belt edge position is accurately recognized, and whether the image semantic segmentation network model achieves a set effect or not is judged when the average intersection-to-union ratio is smaller than or equal to an intersection-to-union ratio threshold value. Adding the belt operation images with the intersection-to-union ratio smaller than the intersection-to-union ratio threshold in the test set into the training set, and continuing to train the model; the model accuracy is continuously improved, all-weather continuous real-time monitoring of the belt running condition is achieved, the accuracy of belt deviation detection is improved, and the stability is high.

The invention discloses a rotating equipment vibration displacement detection method based on machine vision spatial filtering, and the method comprises the steps: collecting a rotating equipment operation video according to an industrial high-speed camera, and obtaining a two-dimensional image frame of rotating equipment; performing graying processing on the two-dimensional image frame to obtain a preprocessed two-dimensional image; selecting a rectangular region image with a preset size from a preset position of the first frame of preprocessed two-dimensional image; performing operation on rectangular area images selected at the same position of the preprocessed two-dimensional image on line frame by frame according to the frame sequence of the video to obtain vibration displacement increments in one or more angle directions as a continuous vibration displacement sequence in the corresponding angle direction; drawing an envelope spectrum according to the continuous vibration displacement sequence in one or more filtering directions; and calculating the theoretical fault characteristic frequency of the rolling bearing, and searching in the obtained envelope spectrum to complete fault type identification. According to the invention, a visual method can be combined with a signal processing technology, so that a barrier between visual information and vibration characteristics is broken.

The invention relates to the technical field of laser cutting processing, and discloses a laser cutting system based on image recognition excess material detection, which comprises the following steps: acquiring a reference polarization fingerprint of an excess material through a calibration module; then a detection module scans the surface of the excess material and collects a data stream containing position, total intensity of scattered light and light intensity signals of an original sector, and when the total intensity of the scattered light is sharply attenuated and the polarization mismatch degree between a scanning Mueller matrix and a reference polarization fingerprint, which is calculated in real time, exceeds a threshold value, the point is determined as a boundary point; and the contour generation module is used for preprocessing and sorting the point clouds and generating a three-dimensional contour model capable of accurately reflecting the surface fluctuation of the excess material. According to the method, the sensitivity of a traditional visual method to illumination and surface defects is effectively overcome, rapid and high-precision detection of the real three-dimensional contour of the excess material is achieved, and the recycling efficiency and the cutting quality of the excess material are remarkably improved.

The invention discloses a net cage fouling degree intelligent identification system and method based on electromagnetic-optical fusion perception, and belongs to the technical field of net cage fouling degree intelligent identification. The system comprises a mobile scanning platform, an integrated multi-mode sensing unit and a central processing unit; the integrated multi-mode sensing unit comprises an optical imaging module, an electromagnetic characteristic detection module and a positioning auxiliary unit. The method comprises the steps of cooperative scanning and multi-modal data acquisition, data fusion and contamination intelligent identification, accurate positioning and coordinate mapping, global contamination mapping, quantitative evaluation and cleaning decision support. According to the invention, through optical and electromagnetic multi-mode perception fusion, the identification precision of the types of the fouling organisms and the quantification accuracy of the coverage rate are greatly improved, and especially in a turbid water body, the limitation of a traditional single vision method is effectively made up; based on the global fouling distribution map generated by the fusion positioning technology, the operation mode change from blind traversal to accurate targeting is realized.